本論文主要內容為嘗試及探討一新的混合式室內定位技術。此混合式室內定位技術利用了慣性導航系統及在一無線感測器網路內以距離估測為基礎之位置演算法所得的位置資訊的優點。本論文的主要目標在於利用上述方法建立一準確，微小誤差之定位方法。在本論文中，距離估測方面將利用在使用者端量測得的訊號強度當作一指標，隨著距離的衰減而推測出對應的距離。使用此方法時，訊號衰減模型參數的選擇便顯得十分重要。為此，本論文提出了一簡單直覺但實用的自我參數決定法，使系統可自行決定對應該使用環境適合的參數。

　　本論文使用卡曼濾波器及ILS濾波器來混合慣性導航系統及無線感測器網路定位系統之位置資訊。利用距離量測為基礎的無線感測器網路定位系統所得之實驗結果；利用慣性導航系統所得之實驗結果及混合後之結果皆會於本論文中呈現。使用慣性導航系統所求得之位置資訊比利用距離量測為基礎的無線感測器網路定位系統還要精確，然而其求得之位置為相對位置，因此必須和無線感測器網路定位系統合併，以其達到最高的精確度。

This thesis investigates a new fused indoor position determination technique. The fused position determination technique takes the advantages of position information from measurements of Inertial Measurement Unit (IMU) and the distance prediction based positioning algorithm in a wireless sensor network. The goal of this thesis is to obtain an accurate, drift-free positioning method indoors by utilizing the technique mentioned above. In this thesis, the distance prediction based positioning algorithm uses degradation of received signal strength (RSS) in the user end as an indication of distance between beacons and the user in a wireless sensor network. Four sensors in the network act as beacons. A signal propagation model must be utilized to transfer the RSS information into distance. As a consequence, no matter what kind of signal propagation model is chosen, one must determine the parameters of signal propagation model before utilizing it. This thesis uses a simple, straightforward but effective self-parameter-determination technique to determine the parameters of the signal propagation model at different environments in a wireless sensor network.

This thesis tries to fuse the position information obtained from the distance prediction based positioning algorithm and IMU using Kalman filter for static case and Iterated Least Squares (ILS) filter for dynamic case. The experimental results by utilizing position information obtained from distance prediction based positioning algorithm, measurements of IMU and the fused method will be shown. The position information obtained from IMU is relatively accurate in comparison with which obtained from the distance prediction based positioning algorithm. However, only distance prediction based positioning algorithm can determine the absolute position of a user in a wireless sensor network. By fusing the two methods, the accuracy of the fused positioning system is about 1m RMS error.

1. Applications of Rosum TV-GPS system, http://www.rosum.com/index.html
2. FCC E911 summary, http://www.fcc.gov/911/enhanced
3. Intel, An Introduction to Wireless Sensor Networks, http://www.intel.com/research/exploratory/instrument_world.htm
4. Brown, Robert Grover, Hwang, Patrick Y.C., Introduction to Random Signals and Applied Kalman Filtering: with MATLAB exercises and solutions, John Wiley & Sons, Inc.
5. D.H. Titterton and J.L.Weston, Strapdown Inertial Navigation Technoloty, Peter Peregrinus Ltd.
6. Mohinder S. Grewal, Lawrence R. Weill, Angus P. Andrews, Global Positioning Systems, Inertial Navigation, and Integration, John Wiley & Sons, Inc
7. P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In Proc. IEEE Infocom, pages 775–784, Tel-Aviv, Israel, April 2000.
8. M. Berna, B. Lisien, B. Sellner, G. Gordon, F. Pfenning, and S. Thrun. A
learning algorithm for localizing people based on wireless signal strength
that uses labeled and unlabeled data. In Proceedings of IJCAI 03, pages 1427-1428, 2003
9. P. Castro, P. Chiu, T. Kremenek, and R. Muntz. A Probabilistic Location
Service for Wireless Network Environments. Ubiquitous Computing
2001, September 2001.
10. P. Bergamo and G. Mazzini. Localization in sensor networks with fading and mobility. In The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC02), September 2002.
11. D.Niculescu and B. Nath. Vor base stations for indoor 802.11 positioning.
In Proceedings of Mobicom, 2004.
12. E. Elnahrawy, X. Li, and R. M. Martin. The limits of localization using signal strength: A comparative study. In Proceedings of Sensor and Ad-Hoc Communications and Networks Conference (SECON), Santa Clara California, October 2004.
13. Dimitrios Lymberopoulos, Quentin Lindsey and Andreas Savvides, An Empirical Analysis of Radio Signal Strength Variability in IEEE 802.15.4 Networks using Monopole Antennas, Embedded Networks and Applications Lab, Yale University
14. Crossbow Mica Getting Started Guide, Document 7430-0022-07, Rev. A, September 2005
15. ZigBee specification, http://www.zigbee.org/
16. Philip Levis, Sam Madden, David gay, Joseph Polastre, Robert Szewczyk, Alec Woo, Eric Brewer and David Culler, “The Emergence of Networking Abstractions and Techniques in TinyOS”
17. Deepak Ganesan, Bhaskar Krishnamachari, Alec Woo, David Culler, Deborah Estrin, and Stephen Wicker. Complex behavior at scale: An experimental study of low-power wireless sensor networks. UCLA Computer Science Technical Report UCLA/CSD-TR 02-0013.
18. Cameron Dean Whitehouse, The Design of Calamari: an Ad-hoc Localization System for Sensor Networks
19. Andy Harter, Andy Hopper, Pete Steggles, Andy Ward, and Paul Webster. The anatomy of a context-aware application. In Mobile Computing and Networking, pages 59–68, 1999.

20. Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. The cricket location-support system. In Mobile Computing and Networking, pages 32–43, 2000.
21. A. Savvides, H. Park, and M. B. Srivastava. The bits and flops of the n-hop multilateration primitive for node localization problems. In First ACM International Workshop on Sensor Networks and Applications, Septmber 2002.
22. et. al Luis Navarro-Serment. A beacon system for the localization of distributed robotic teams. In the International Conference on Field and Service Robotics, Pittsburgh, PA, August 1999.
23. A. Goldsmith, Wireless Communication, 2005
24. J. Beutel, Geolocation in a PicoRadio Environment, MS Thesis, ETH Zurich,
December, 1999.
25. Bradford W. Parkinson, James J. Spilker Jr., Global Positioning System: Theory and Applications volume 1.11, AIAA
26. Edward A. LeMaster, Self-Calibrating Pseudolite Arrays: Theory and Experiment
27. Lecture Handout of Navigation and Guidance Systems, Autumn 2004